OpenRaiser/Intern-Atlas huggingface dataset:
A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
Venue: arXiv cs.CV 2018
Year: 2018

External localization is an essential part for the indoor operation of small or cost-efficient robots, as they are used, for example, in swarm robotics. We introduce a two-stage localization and instance identification framework for arbitrary robots based on convolutional neural networks. Object detection is performed on an external camera image of the operation zone providing robot bounding boxes for an identification and orientation estimation convolutional neural network. Additionally, we propose a process to generate the necessary training data. The framework was evaluated with 3 different robot types and various identification patterns. We have analyzed the main framework hyperparameters providing recommendations for the framework operation settings. We achieved up to 98% mAP@IOU0.5 and only 1.6° orientation error, running with a frame rate of 50 Hz on a GPU.
New entanglement-assisted MDS quantum codes from constacyclic codes
Venue: arXiv cs.IT 2018
Year: 2018

Construction of good quantum codes via classical codes is an important task for quantum information and quantum computing. In this work, by virtue of a decomposition of the defining set of constacyclic codes we have constructed eight new classes of entanglement-assisted quantum maximum distance separable codes.
2018 Low-Power Image Recognition Challenge
Venue: arXiv cs.CV 2018
Year: 2018

The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners' scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners' solutions.
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Recent techniques in self-supervised monocular depth estimation are approaching the performance of supervised methods, but operate in low resolution only. We show that high resolution is key towards high-fidelity self-supervised monocular depth prediction. Inspired by recent deep learning methods for Single-Image Super-Resolution, we propose a sub-pixel convolutional layer extension for depth super-resolution that accurately synthesizes high-resolution disparities from their corresponding low-resolution convolutional features. In addition, we introduce a differentiable flip-augmentation layer that accurately fuses predictions from the image and its horizontally flipped version, reducing the effect of left and right shadow regions generated in the disparity map due to occlusions. Both contributions provide significant performance gains over the state-of-the-art in self-supervised depth and pose estimation on the public KITTI benchmark. A video of our approach can be found at https://youtu.be/jKNgBeBMx0I.
GINN: Geometric Illustration of Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

This informal technical report details the geometric illustration of decision boundaries for ReLU units in a three layer fully connected neural network. The network is designed and trained to predict pixel intensity from an (x, y) input location. The Geometric Illustration of Neural Networks (GINN) tool was built to visualise and track the points at which ReLU units switch from being active to off (or vice versa) as the network undergoes training. Several phenomenon were observed and are discussed herein. This technical report is a supporting document to the blog post with online demos and is available at http://www.bayeswatch.com/2018/09/17/GINN/.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Venue: arXiv cs.AI 2018
Year: 2018

Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license {https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience (https://aif360.mybluemix.net) that provides a gentle introduction to the concepts and capabilities for line-of-business users, as well as extensive documentation, usage guidance, and industry-specific tutorials to enable data scientists and practitioners to incorporate the most appropriate tool for their problem into their work products. The architecture of the package has been engineered to conform to a standard paradigm used in data science, thereby further improving usability for practitioners. Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality.
A Comparison of I/O-Efficient Algorithms for Visibility Computation on Massive Grid Terrains
Venue: arXiv cs.DS 2018
Year: 2018

Given a grid terrain T and a viewpoint v, the viewshed of v is the set of grid points of T that are visible from v. To decide whether a point p is visible one needs to interpolate the elevation of the terrain along the line-of-sight vp. Existing viewshed algorithms differ widely in what points they chose to interpolate and how they interpolate the terrain. These choices crucially affect the running time and accuracy of the algorithms. This paper describes I/O-efficient algorithms for computing visibility maps in a couple of different models. First, we describe two algorithms that sweep the terrain by rotating a ray around the viewpoint while maintaining the terrain profile along the ray. Second, we describe an algorithm which sweeps the terrain centrifugally, growing a star-shaped region around the viewpoint while maintaining the approximate visible horizon of the terrain within the swept region. Our last two algorithms are based on computing and merging horizons. All algorithms are I/O-efficient in the I/O-model of Aggarwal and Vitter. We present an experimental analysis on large terrains obtained from NASA SRTM data. All our algorithms are scalable to volumes of data that are over 50 times larger than main memory. Our main finding is that, in practice, horizons are significantly smaller than their theoretical worst case bound, which makes horizon-based approaches very fast. Our last two algorithms, which compute the most accurate viewshed, turn out to be very fast in practice, although their worst-case bound is inferior.
Reinforcement Learning Meets Hybrid Zero Dynamics: A Case Study for RABBIT
Venue: arXiv cs.RO 2018
Year: 2018

The design of feedback controllers for bipedal robots is challenging due to the hybrid nature of its dynamics and the complexity imposed by high-dimensional bipedal models. In this paper, we present a novel approach for the design of feedback controllers using Reinforcement Learning (RL) and Hybrid Zero Dynamics (HZD). Existing RL approaches for bipedal walking are inefficient as they do not consider the underlying physics, often requires substantial training, and the resulting controller may not be applicable to real robots. HZD is a powerful tool for bipedal control with local stability guarantees of the walking limit cycles. In this paper, we propose a non traditional RL structure that embeds the HZD framework into the policy learning. More specifically, we propose to use RL to find a control policy that maps from the robot's reduced order states to a set of parameters that define the desired trajectories for the robot's joints through the virtual constraints. Then, these trajectories are tracked using an adaptive PD controller. The method results in a stable and robust control policy that is able to track variable speed within a continuous interval. Robustness of the policy is evaluated by applying external forces to the torso of the robot. The proposed RL framework is implemented and demonstrated in OpenAI Gym with the MuJoCo physics engine based on the well-known RABBIT robot model.
Action Model Acquisition using LSTM
Venue: arXiv cs.AI 2018
Year: 2018

In the field of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems. It is, however, becoming increasingly cumbersome to codify this model, and is more efficient to learn it from observed plan execution sequences (training data). While the underlying objective is to subsequently plan from this learnt model, most approaches fall short as anything less than a flawless reconstruction of the underlying model renders it unusable in certain domains. This work presents a novel approach using long short-term memory (LSTM) techniques for the acquisition of the underlying action model. We use the sequence labelling capabilities of LSTMs to isolate from an exhaustive model set a model identical to the one responsible for producing the training data. This isolation capability renders our approach as an effective one.
Domain Specific Approximation for Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this work, we investigate the trade-off between accuracy and speed with domain-specific approximations, i.e. category-aware image size scaling and proposals scaling, for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand the potential and the applicability of them. By conducting experiments on the ImageNet VID dataset, we show that domain-specific approximation has great potential to improve the speed of the system without deteriorating the accuracy of object detectors, i.e. up to 7.5x speedup for dynamic domain-specific approximation. To this end, we present our insights toward harvesting domain-specific approximation as well as devise a proof-of-concept runtime, AutoFocus, that exploits dynamic domain-specific approximation.
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining. The recent advances in graph neural networks have made automatic and flexible feature extraction from graphs possible and have improved the predictive performance significantly. In this paper, we go further with this line of research and address a more general problem of learning with a graph of graphs (GoG) consisting of an external graph and internal graphs, where each node in the external graph has an internal graph structure. We propose a dual convolutional neural network that extracts node representations by combining the external and internal graph structures in an end-to-end manner. Experiments on link prediction tasks using several chemical network datasets demonstrate the effectiveness of the proposed method.
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Venue: arXiv cs.CV 2018
Year: 2018

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.
Learning Finer-class Networks for Universal Representations
Venue: arXiv cs.CV 2018
Year: 2018

Many real-world visual recognition use-cases can not directly benefit from state-of-the-art CNN-based approaches because of the lack of many annotated data. The usual approach to deal with this is to transfer a representation pre-learned on a large annotated source-task onto a target-task of interest. This raises the question of how well the original representation is "universal", that is to say directly adapted to many different target-tasks. To improve such universality, the state-of-the-art consists in training networks on a diversified source problem, that is modified either by adding generic or specific categories to the initial set of categories. In this vein, we proposed a method that exploits finer-classes than the most specific ones existing, for which no annotation is available. We rely on unsupervised learning and a bottom-up split and merge strategy. We show that our method learns more universal representations than state-of-the-art, leading to significantly better results on 10 target-tasks from multiple domains, using several network architectures, either alone or combined with networks learned at a coarser semantic level.
Compound Binary Search Tree and Algorithms
Venue: arXiv cs.DS 2018
Year: 2018

The Binary Search Tree (BST) is average in computer science which supports a compact data structure in memory and oneself even conducts a row of quick algorithms, by which people often apply it in dynamical circumstance. Besides these edges, it is also with weakness on its own structure specially with poor performance at worst case. In this paper, we will develop this data structure into a synthesis to show a series of novel features residing in. Of that, there are new methods invented for raising the performance and efficiency nevertheless some existing ones in logarithm or linear time.
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Venue: arXiv cs.GR 2018
Year: 2018

We propose a novel approach for performing convolution of signals on curved surfaces and show its utility in a variety of geometric deep learning applications. Key to our construction is the notion of directional functions defined on the surface, which extend the classic real-valued signals and which can be naturally convolved with with real-valued template functions. As a result, rather than trying to fix a canonical orientation or only keeping the maximal response across all alignments of a 2D template at every point of the surface, as done in previous works, we show how information across all rotations can be kept across different layers of the neural network. Our construction, which we call multi-directional geodesic convolution, or directional convolution for short, allows, in particular, to propagate and relate directional information across layers and thus different regions on the shape. We first define directional convolution in the continuous setting, prove its key properties and then show how it can be implemented in practice, for shapes represented as triangle meshes. We evaluate directional convolution in a wide variety of learning scenarios ranging from classification of signals on surfaces, to shape segmentation and shape matching, where we show a significant improvement over several baselines.
Information Market for Web Browsing: Design, Usability and Incremental Adoption
Venue: arXiv cs.NI 2018
Year: 2018

Browsing privacy solutions face an uphill battle to deployment. Many operate counter to the economic objectives of popular online services (e.g., by completely blocking ads) and do not provide enough incentive for users who may be subject to performance degradation for deploying them. In this study, we take a step towards realizing a system for online privacy that is mutually beneficial to users and online advertisers: an information market. This system not only maintains economic viability for online services, but also provides users with financial compensation to encourage them to participate. We prototype and evaluate an information market that provides privacy and revenue to users while preserving and sometimes improving their Web performance. We evaluate feasibility of the market via a one month field study with 63 users and find that users are indeed willing to sell their browsing information. We also use Web traces of millions of users to drive a simulation study to evaluate the system at scale. We find that the system can indeed be profitable to both users and online advertisers.
Computer vision-based framework for extracting geological lineaments from optical remote sensing data
Venue: arXiv cs.CV 2018
Year: 2018

The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.
A method to Suppress Facial Expression in Posed and Spontaneous Videos
Venue: arXiv cs.CV 2018
Year: 2018

We address the problem of suppressing facial expressions in videos because expressions can hinder the retrieval of important information in applications such as face recognition. To achieve this, we present an optical strain suppression method that removes any facial expression without requiring training for a specific expression. For each frame in a video, an optical strain map that provides the strain magnitude value at each pixel is generated; this strain map is then utilized to neutralize the expression by replacing pixels of high strain values with pixels from a reference face frame. Experimental results of testing the method on various expressions namely happiness, sadness, and anger for two publicly available data sets (i.e., BU-4DFE and AM-FED) show the ability of our method in suppressing facial expressions.
On Urgency in Asynchronous Timed Session Types
Venue: arXiv cs.LO 2018
Year: 2018

We study an urgent semantics of asynchronous timed session types, where input actions happen as soon as possible. We show that with this semantics we can recover to the timed setting an appealing property of untimed session types: namely, deadlock-freedom is preserved when passing from synchronous to asynchronous communication.
Meeting Real-Time Constraint of Spectrum Management in TV Black-Space Access
Venue: arXiv cs.NI 2018
Year: 2018

The TV set feedback feature standardized in the next generation TV system, ATSC 3.0, would enable opportunistic access of active TV channels in future Cognitive Radio Networks. This new dynamic spectrum access approach is named as black-space access, as it is complementary of current TV white space, which stands for inactive TV channels. TV black-space access can significantly increase the available spectrum of Cognitive Radio Networks in populated urban markets, where spectrum shortage is most severe while TV whitespace is very limited. However, to enable TV black-space access, secondary user has to evacuate a TV channel in a timely manner when TV user comes in. Such strict real-time constraint is an unique challenge of spectrum management infrastructure of Cognitive Radio Networks. In this paper, the real-time performance of spectrum management with regard to the degree of centralization of infrastructure is modeled and tested. Based on collected empirical network latency and database response time, we analyze the average evacuation time under four structures of spectrum management infrastructure: fully distribution, city-wide centralization, national-wide centralization, and semi-national centralization. The results show that national wide centralization may not meet the real-time requirement, while semi-national centralization that use multiple co-located independent spectrum manager can achieve real-time performance while keep most of the operational advantage of fully centralized structure.
PAPR Reduction in OFDM-IM Using Multilevel Dither Signals
Venue: arXiv cs.IT 2018
Year: 2018

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel multicarrier scheme, which uses the indices of the active subcarriers to transmit data. OFDM-IM also inherits the high peak-to-average power ratio (PAPR) problem, which induces in-band distortion and out-of-band radiation when OFDM-IM signal passes through high power amplifier (HPA). In this letter, dither signals are added in the idle subcarriers to reduce the PAPR. Unlike the previous work using single level dither signals, multilevel dither signals are used by exploiting that the amplitudes of the active subcarriers are variously distributed for different subblocks. As a result, the proposed scheme gives the dither signals more freedom (a larger radius of dithering) in average. Simulation results show that the proposed scheme can achieve better PAPR reduction performance than the previous work.